joseph.ergo@proton.me | Portfolio | Resume PDF | Linked-In | +212 713-617-633

Available immediately for full/part-time remote roles

FRESHWORKS INC. (FORMERLY FRESHDESK)

Table of contents

• 1 About the project
• 2 EDA
  • 2.1 information technology and services indestry’s yearly new recruit count
  • 2.2 freshworks inc. (formerly freshdesk)’s workforce status over the years
• 3 Persona company network graph
• 4 Workforce sample
  • 4.1 Abhishek Madhavan
  • 4.2 Adit Parekh
  • 4.3 Ali Murphy
  • 4.4 Amiya Adwitiya
  • 4.5 Andrews Moses
  • 4.6 Anna Hantelmann
  • 4.7 Arjun Sukumaran
  • 4.8 Ashwin Ramesh
  • 4.9 Christopher Wiltberger
  • 4.10 Daniel Nath
  • 4.11 Danielle Murphy
  • 4.12 Dhawal Kumar
  • 4.13 Duncan Travis
  • 4.14 Eli Thomas
  • 4.15 Erik Bromley
  • 4.16 Indeevar Krishna
  • 4.17 Joe Massian
  • 4.18 Karthick Sundaram
  • 4.19 Lauren Miller
  • 4.20 Marina Sweeney
  • 4.21 Miguel Adao
  • 4.22 Nirmaan Agrawal
  • 4.23 Nivas Ravichandran
  • 4.24 Pranav Mehra
  • 4.25 Saurav Chakravorty
  • 4.26 Shekhar Kirani
  • 4.27 Srividya Sriram
  • 4.28 Subhash Chandran
  • 4.29 Susan Callaghan
  • 4.30 Vikram Naval

## SETUP
from pathlib import Path
import duckdb
from tqdm.notebook import tqdm
import datetime
import copy
import polars as pl
import plotly.express as px
import plotly.io as pio
import re
from concurrent.futures import ThreadPoolExecutor
import plotly.graph_objects as go
import networkx as nx
import numpy as np
# pio.renderers.default = 'plotly_mimetype'
pio.renderers.default = 'jupyterlab+notebook'
pio.templates.default = "plotly_white"

path_data = Path.cwd()/'data'/'03_rdb'
path_data_companies = path_data/'companies_table.parquet'
path_data_experience = path_data/'experience_table.parquet'
path_data_emails = path_data/'emails_table.parquet'
path_data_education = path_data/'education_table.parquet'
path_data_school = path_data/'school_table.parquet'
path_data_persona = path_data/'persona_table.parquet'
path_data_profiles = path_data/'profiles_table.parquet'

path_output_images = Path.cwd()/'output'/'images'

conn = duckdb.connect()

conn.execute("SET temp_directory = 'temp';")
conn.execute("SET memory_limit = '10GB';")
conn.execute("SET max_temp_directory_size = '100GB';")
conn.execute("SET threads = 8;")
conn.execute("SET preserve_insertion_order = false;")
conn.execute("SET enable_progress_bar = true;")
conn.execute("SET enable_progress_bar_print = true;")

df = pl.read_parquet('03_target_companies3.parquet')
df_yearly_new_hires_per_indestry = pl.read_parquet('03_yearly_new_hires_per_indestry.parquet')

current_company_id = "&-friends"
current_company_id = pl.read_json("04__control__.json")[0,'current_company_id']
query = f"""
SELECT *
FROM read_parquet('{path_data_companies}')
WHERE company_id = '{current_company_id}'
"""
df_company_by_company_id = pl.DataFrame(conn.execute(query).df())

current_company_name = df_company_by_company_id[0,'company_name']
current_company_indestry = df_company_by_company_id[0,'company_industry']

current_company_parquet = Path.cwd()/'output'/'company_data'/f"{current_company_id}.parquet"

# Info about personas status from company_id

query = f"""
SELECT *
FROM read_parquet('{path_data_experience}')
WHERE company_id = '{current_company_id}'
"""
df_experiences_by_company_id = pl.DataFrame(conn.execute(query).df())

personas_whitout_end_date = df_experiences_by_company_id.filter(pl.col('end_date').is_null())
personas_who_got_raise = df_experiences_by_company_id.filter((pl.col('end_date').is_not_null()) &
                                     pl.col('persona_id').is_in(personas_whitout_end_date['persona_id'].to_list()))
personas_who_stayed = (pl
                      .concat([personas_whitout_end_date, personas_who_got_raise])
                      .sort('start_date')
                      .group_by('persona_id')
                      .agg(
                          pl.col('title_name').last(),
                          pl.col('is_primary').last(),
                          pl.col('start_date').min(),
                          pl.col('end_date').max(),
                          pl.col('title_name').count().alias('changes'),
                          pl.col('title_name').unique().alias('all_title_name'),
                      )
                      .with_columns(
                          pl.lit(True).alias('still_associated'),
                          pl.lit(None).alias('end_date')
                      )
                      .sort('changes')
                             )

personas_who_left = df_experiences_by_company_id.filter((pl.col('end_date').is_not_null()) & ~pl.col('persona_id').is_in(personas_who_stayed['persona_id'].to_list()) )
personas_who_left = (personas_who_left
                     .sort('start_date')
                     .group_by('persona_id')
                     .agg(
                          pl.col('title_name').last(),
                          pl.col('is_primary').last(),
                          pl.col('start_date').min(),
                          pl.col('end_date').max(),
                          pl.col('title_name').count().alias('changes'),
                          pl.col('title_name').unique().alias('all_title_name'),
                              )
                     .with_columns(
                         pl.lit(False).alias('still_associated'),
                         
                     )
                     .sort('changes'))

df_personas_who_worked_in_company = pl.concat([personas_who_stayed, personas_who_left], how='vertical_relaxed').with_columns(
    (pl.col('end_date').dt.year()-pl.col('start_date').dt.year()).alias('work_durration')
).sort('work_durration')

import dns.resolver
import smtplib
import socket

def check_deliverability(email_address):
    """
    Checks the deliverability of an email address by verifying MX records
    and performing an SMTP connection test.
    """
    if '@' not in email_address:
        return False
    
    domain = email_address.split('@')[1]
    
    # Check for MX records
    try:
        mx_records = dns.resolver.resolve(domain, 'MX')
        if not mx_records:
            return False
    except (dns.resolver.NoAnswer, dns.resolver.NXDOMAIN, dns.resolver.Timeout):
        return False

    # Perform SMTP connection test
    mx_host = str(mx_records[0].exchange)
    
    # Validate MX hostname before attempting connection
    try:
        # Test if hostname can be properly encoded
        mx_host.encode('idna')
    except UnicodeError:
        return False
    
    try:
        with smtplib.SMTP(mx_host, timeout=10) as smtp:
            smtp.set_debuglevel(0)
            smtp.helo(socket.gethostname())
            smtp.mail('test@example.com')
            code, _ = smtp.rcpt(email_address)

            return code == 250  # 250 indicates valid email address
            
    except (smtplib.SMTPException, socket.error, UnicodeError):
        return False

# info of all personas info
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_persona}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas = pl.DataFrame(conn.execute(query).df())

# info of all personas profiles
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_profiles}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_profile = pl.DataFrame(conn.execute(query).df())
df_all_personas_profile_f = df_all_personas_profile.group_by('persona_id').agg(pl.col('url').unique())

# info of all personas email
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")
list_for_in = ', '.join(list_w)

query = f"""
SELECT *
FROM read_parquet('{path_data_emails}')
WHERE persona_id IN ({list_for_in}) AND type == 'personal'
"""
df_all_personas_emails = pl.DataFrame(conn.execute(query).df())

def def_polars_fix_gmail(x):
    if "@gmail" in x:
        first_part = x.split('@')[0]
        second_part = x.split('@')[1]
        return f"{first_part.replace(".",'')}@{second_part}"
    else:
        return x

df_all_personas_emails_f = (df_all_personas_emails
                            .with_columns(pl.col('address')
                                          .map_elements(def_polars_fix_gmail, return_dtype=pl.String)
                                          .alias('normalised_emails'))
                            .unique('normalised_emails', keep='first')
                            .sort('persona_id')
                            .drop('normalised_emails')
                         )
df_all_personas_emails_f = (df_all_personas_emails_f.group_by('persona_id').agg(pl.col('address').unique(),pl.col('type').unique()))
df_all_personas_plus = df_all_personas.join(df_all_personas_emails_f, on='persona_id', how='left')

df_full_personas_who_worked_in_company = (df_personas_who_worked_in_company
                                       .join(df_all_personas_plus, on='persona_id', how='left')
                                       .join(df_all_personas_profile_f, on='persona_id', how='left')
                                      )

df_full_personas_who_worked_in_company = (
    df_full_personas_who_worked_in_company.with_columns(
        (pl.col("start_date").fill_null(pl.col("start_date").min()))
        .dt.year()
        .alias("start_year"),
        (pl.col("end_date").dt.year()).alias("end_year"),
    )
)

work_years = []
for i in range(len(df_full_personas_who_worked_in_company)):
    start_y = df_full_personas_who_worked_in_company[i, "start_year"]
    if df_full_personas_who_worked_in_company[i, "end_year"]:
        end_y = df_full_personas_who_worked_in_company[i, "end_year"]
    else:
        end_y = 2020

    tmp_work_years = []
    for y in range(start_y, end_y + 1):
        tmp_work_years.append(y)

    work_years.append(tmp_work_years)

df_full_personas_who_worked_in_company = (
    df_full_personas_who_worked_in_company.with_columns(
        pl.Series("work_years", work_years)
    )
)

# add hireups
title_name_match = ["ceo","chief","founder","owner","president","vp","vice","director",
    "cfo","cto","partner","head of","hr ","human","talent","senior","manager","lead"]

df_full_personas_who_worked_in_company = (df_full_personas_who_worked_in_company
    .with_columns(
        pl.when(pl.col('title_name').str.contains_any(title_name_match)).then(True).otherwise(False).alias("higher_up")
    ))



df_tmp_email_checker = (
    df_full_personas_who_worked_in_company
    .filter(
            pl.col('still_associated')==True,
            pl.col('address').list.len()>0
    )
        ['persona_id','address']
        .explode('address')
)

# if current_company_parquet.exists():
#     df_pre_full_personas_who_worked_in_company = pl.read_parquet(current_company_parquet)
#     list_pre_deliverable_address = df_pre_full_personas_who_worked_in_company['address'].drop_nulls().explode().to_list()
# else:
#     list_pre_deliverable_address = []

# list_of_emails_to_check = df_tmp_email_checker['address'].drop_nulls().to_list()
# list_lists_email_check = []

# var_total_emails = len(list_of_emails_to_check)
# var_current_email_count = 0

# def def_check_and_populate(email_to_check):
#     global list_lists_email_check, var_current_email_count
#     if email_to_check in list_pre_deliverable_address:
#         list_lists_email_check.append([email_to_check, True])
#     elif '@gmail' in email_to_check:
#         list_lists_email_check.append([email_to_check, True])
#     else:
#         try:
#             is_deliverable = check_deliverability(email_to_check)
#             list_lists_email_check.append([email_to_check, is_deliverable])
#         except:
#             list_lists_email_check.append([email_to_check, False])
#     var_current_email_count += 1
#     print(' '*10, end='\r')
#     print(round(var_current_email_count/var_total_emails,5), end='\r')

# with ThreadPoolExecutor(max_workers=20) as executor:
#     results = list(executor.map(def_check_and_populate, list_of_emails_to_check))

# df_email_check = pl.DataFrame(list_lists_email_check, schema=["address", "deliverable"], orient="row")
# try:
#     df_tmp_email_checker_f = (
#         df_tmp_email_checker
#             .join(df_email_check, on='address')
#             .filter(pl.col('deliverable')==True)
#             .group_by('persona_id').agg(pl.col('address').unique().alias("deliverable_address"))
#     )
# except:
#     df_tmp_email_checker_f = pl.DataFrame()

# if df_tmp_email_checker_f.is_empty():
#     df_full_personas_who_worked_in_company = df_full_personas_who_worked_in_company.join(df_tmp_email_checker.rename({'address':'deliverable_address'}), on="persona_id", how='left')
# else:
#     df_full_personas_who_worked_in_company = df_full_personas_who_worked_in_company.join(df_tmp_email_checker_f, on="persona_id", how='left')

# Info about personas experiences

# info of all experiences[]
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT *
FROM read_parquet('{path_data_experience}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_experiences = pl.DataFrame(conn.execute(query).df())


# info of all comapnies in said experiences
list_w = []
for word in df_all_personas_experiences['company_id'].unique().to_list():
    if "'" not in word:
        list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT company_id, company_name, company_industry, company_linkedin_url, company_location_country
FROM read_parquet('{path_data_companies}')
WHERE company_id IN ({list_for_in})
"""
df_all_companies = pl.DataFrame(conn.execute(query).df())

df_full_personas_experiences_plus = df_all_personas_experiences.join(df_all_companies, on='company_id', how='left')

df_full_personas_experiences_plus = (
    df_full_personas_experiences_plus
    .with_columns(
        pl.when(
            pl.col('company_id')==current_company_id
        )
        .then(True)
        .otherwise(False)
        .alias('target')
    )
)

# Info about personas education

# info of all experiences
list_w = []
for word in df_experiences_by_company_id['persona_id'].unique().to_list():
    list_w.append(f"'{word}'")

list_for_in = ', '.join(list_w)
query = f"""
SELECT *
FROM read_parquet('{path_data_education}')
WHERE persona_id IN ({list_for_in})
"""
df_all_personas_education = pl.DataFrame(conn.execute(query).df())


#ifon of allcomapnies in said experiences
list_w = []
for word in df_all_personas_education['school_id'].unique().to_list():
    if "'" not in word:
        list_w.append(f"'{word}'")

if list_w:
    list_for_in = ', '.join(list_w)
    query = f"""
    SELECT school_id, school_name, school_type, school_website, school_location_country
    FROM read_parquet('{path_data_school}')
    WHERE school_id IN ({list_for_in})
    """
    df_all_school = pl.DataFrame(conn.execute(query).df())
    
    df_full_personas_education_plus = df_all_personas_education.join(df_all_school, on='school_id', how='left')
else:
    df_full_personas_education_plus = df_all_personas_education

1 About the project

The project came to life after realizing that web scraping doesn’t allow deep-level filtering—without consuming too much time.The irony is, this project itself took me about a month, but the final RDB contains more data than I could ever scrape.

The raw data was 1.4 TB in size and holds information previously scraped.
Processing was done on my local machine using Python, Polars, and DuckDB, following this workflow:
- Processed raw data into structured Parquet files using Polars.
- Transformed each Parquet file into mini RDBs using Polars.
- Merged all mini RDBs into one using DuckDB.
- Analyzed and filtered data to fit the current project.

2 EDA

2.1 information technology and services indestry’s yearly new recruit count

list_of_unique_company_experience_years = []
for y in df_full_personas_who_worked_in_company['start_year'].unique().drop_nulls().to_list():
    if y not in list_of_unique_company_experience_years:
        list_of_unique_company_experience_years.append(y)
for y in df_full_personas_who_worked_in_company['end_year'].unique().drop_nulls().to_list():
    if y not in list_of_unique_company_experience_years:
        list_of_unique_company_experience_years.append(y)

list_year = []
list_state = []
list_count = []
list_names = []

def def_get_names_breked(tmp):
    if tmp.is_empty():
        names_string = ''
    else:
        tmp_list_name = []
        names_limit = 3
        row_limit = names_limit * 6
        for i, name in enumerate(tmp['full_name'].to_list()):
            ii = i+1
            tmp_list_name.append(name.title())
            if ii!=0 and ii%names_limit==0:
                tmp_list_name.append("<br>")
            if ii==row_limit:
                tmp_list_name.append("...")
                break
        names_string = ', '.join(tmp_list_name).replace(", <br>, ","<br>")
    return names_string

for y in list_of_unique_company_experience_years:
    #recuite state
    list_year.append(y)
    list_state.append('Recruited')
    tmp = df_full_personas_who_worked_in_company.filter(pl.col('start_year')==y).sort('full_name')
    list_count.append(len(tmp))
    list_names.append(def_get_names_breked(tmp))
    
    #recuite state
    list_year.append(y)
    list_state.append('Resigned')
    tmp = df_full_personas_who_worked_in_company.filter(pl.col('end_year')==y).sort('full_name')
    list_count.append(len(tmp))
    list_names.append(def_get_names_breked(tmp))

df_m_recruite_vs_resign = pl.DataFrame({
    'year':list_year,
    'status':list_state,
    'count':list_count,
    'names':list_names,})

2.2 freshworks inc. (formerly freshdesk)’s workforce status over the years

3 Persona company network graph

gr_net = df_full_personas_experiences_plus.with_columns(pl.col('company_id').str.to_uppercase()).group_by('persona_id','company_id').agg(pl.len().alias('count')).sort('count')
list_top_in_network = gr_net['company_id'].value_counts().sort('count', descending=True)['company_id'].to_list()[:5]
gr_net_f = gr_net.filter(pl.col('company_id').is_in(list_top_in_network))

list_letters = ['A','B','C','D','E','F','G','H']
dict_company = {}
dict_company_rev = {}
for company, letter in zip(list_top_in_network, list_letters ):
    dict_company[letter] = company
    dict_company_rev[company] = letter

gr_gr_net_f = gr_net_f.sort('company_id').group_by('persona_id').agg(pl.col('company_id').unique().sort(),)

gr_gr_net_f2 = (
    gr_gr_net_f['company_id']
    .value_counts()
    .with_columns(
        # pl.col('company_id').list.join(', '),
        (pl.col('count')/len(gr_gr_net_f)).alias('per')
    )
    .sort('per',descending=True)
)

list_prob = []
for i in range(len(gr_gr_net_f2)):
    tmp_prob_letters = []
    for k in dict_company.keys():
        if dict_company[k] in gr_gr_net_f2[i]['company_id'][0].to_list():
            tmp_prob_letters.append(f' {k}')
        else:
            tmp_prob_letters.append(f'¬{k}')

    list_prob.append(f"P({' ∩ '.join(tmp_prob_letters)}) = {round(gr_gr_net_f2[i]['per'][0],4)}")
annon_prob_text = "<b>Probability Distribution:</b><br>" + '<br>'.join(list_prob)



# Create network graph
G = nx.Graph()
for persona, company in gr_net_f.select(['persona_id', 'company_id']).iter_rows():
    G.add_edge(persona, company)

# Get unique values
persona_ids = gr_net_f['persona_id'].unique().to_list()
company_ids = gr_net_f['company_id'].unique().to_list()

# Calculate degrees (connection counts)
degree_dict = dict(G.degree())

# Get min and max degrees for scaling
company_degrees = [degree_dict[c] for c in company_ids]
persona_degrees = [degree_dict[p] for p in persona_ids]

min_company_degree = min(company_degrees) if company_degrees else 1
max_company_degree = max(company_degrees) if company_degrees else 1
min_persona_degree = min(persona_degrees) if persona_degrees else 1
max_persona_degree = max(persona_degrees) if persona_degrees else 1

# Define size ranges
COMPANY_MIN_SIZE = 25
COMPANY_MAX_SIZE = 100
PERSONA_MIN_SIZE = 5
PERSONA_MAX_SIZE = 20

# print(f"Company connections range: {min_company_degree} - {max_company_degree}")
# print(f"Persona connections range: {min_persona_degree} - {max_persona_degree}")

# Sort companies by degree (size) in descending order
company_ids_sorted = sorted(company_ids, key=lambda x: degree_dict[x], reverse=True)

# Check if "Nokia" exists in the data
HIGHLIGHTED_COMPANY = current_company_id
HIGHLIGHTED_COMPANY_EXISTS = HIGHLIGHTED_COMPANY.lower() in [str(c).lower() for c in company_ids]

if HIGHLIGHTED_COMPANY_EXISTS:
    # Get the actual case-sensitive name
    highlighted_company = next(c for c in company_ids if str(c).lower() == HIGHLIGHTED_COMPANY.lower())
    # print(f"Highlighting company: {highlighted_company} (with {degree_dict[highlighted_company]} connections)")
else:
    # print(f"Warning: '{HIGHLIGHTED_COMPANY}' not found in company list")
    highlighted_company = None

# Create layout (companies on outer circle, ordered by size)
pos = {}
num_companies = len(company_ids_sorted)
radius_outer = 2.0

# Position companies on circle, ordered by size (largest first)
for i, company in enumerate(company_ids_sorted):
    # Start at top (90° or π/2 radians) and go counter-clockwise (add angle)
    # Counter-clockwise rotation: angle = start_angle + (i * 2π / num_companies)
    # This puts largest at top, next on left, then bottom, then right
    start_angle = np.pi / 2  # 90° at top
    
    # For counter-clockwise rotation
    angle = start_angle - (2 * np.pi * i / num_companies)
    
    # Convert to x, y coordinates
    pos[company] = (radius_outer * np.cos(angle), radius_outer * np.sin(angle))

# Position personas
for i, persona in enumerate(persona_ids):
    connected_companies = [c for c in company_ids if G.has_edge(persona, c)]
    if connected_companies:
        avg_x = np.mean([pos[c][0] for c in connected_companies])
        avg_y = np.mean([pos[c][1] for c in connected_companies])
        # Add jitter to spread out personas
        jitter_x = np.random.uniform(-0.2, 0.2)
        jitter_y = np.random.uniform(-0.2, 0.2)
        pos[persona] = (avg_x * 0.5 + jitter_x, avg_y * 0.5 + jitter_y)
    else:
        pos[persona] = (0, 0)

# Prepare edge traces
edge_x, edge_y = [], []
for edge in G.edges():
    x0, y0 = pos[edge[0]]
    x1, y1 = pos[edge[1]]
    edge_x.extend([x0, x1, None])
    edge_y.extend([y0, y1, None])

edge_trace = go.Scatter(
    x=edge_x, y=edge_y,
    line=dict(width=0.6, color='rgba(120, 120, 120, 0.15)'),
    hoverinfo='none',
    mode='lines')

# Prepare node traces with proportional sizing
company_x, company_y, company_text = [], [], []
company_color, company_size, company_hover = [], [], []
company_border_width = []  # For border thickness
company_border_color = []  # For border color

persona_x, persona_y = [], []
persona_color, persona_size, persona_hover = [], [], []

# Helper function to scale size proportionally
def scale_size(value, min_val, max_val, min_size, max_size):
    if max_val == min_val:
        return (min_size + max_size) / 2
    return min_size + (value - min_val) / (max_val - min_val) * (max_size - min_size)

# Add COMPANY nodes in sorted order (largest first)
for company in company_ids_sorted:
    x, y = pos[company]
    company_x.append(x)
    company_y.append(y)
    company_text.append(str(company))
    company_color.append('#EF553B')
    
    connections = degree_dict[company]
    # Scale size based on connection count
    scaled_size = scale_size(
        connections, 
        min_company_degree, 
        max_company_degree,
        COMPANY_MIN_SIZE, 
        COMPANY_MAX_SIZE
    )
    company_size.append(scaled_size)
    
    # Custom border for highlighted company
    if highlighted_company and company == highlighted_company:
        company_border_width.append(4)  # Thicker border
        company_border_color.append('#000000')  # Black border
    else:
        company_border_width.append(1)
        company_border_color.append('#000000')
    
    # Hover text
    personas = gr_net_f.filter(pl.col('company_id') == company)['persona_id'].to_list()
    rank = company_ids_sorted.index(company) + 1
    hover_text = f"<b>Company #{rank}:</b> {company}<br>"
    hover_text += f"<b>Personas worked here:</b> {connections}<br>"
    hover_text += f"<b>Connection rank:</b> {rank}/{len(company_ids_sorted)}<br>"
    if connections > 0:
        for persona in personas[:5]:
            persona_name = df_all_personas.filter(pl.col('persona_id')==persona)['full_name'][0].title()
            hover_text += f" • {persona_name}<br>"
        if connections > 5:
            hover_text += f" • ... and {connections - 5} more"
    company_hover.append(hover_text)

# Add PERSONA nodes
for persona in persona_ids:
    x, y = pos[persona]
    persona_x.append(x)
    persona_y.append(y)
    persona_color.append('#636efa')
    
    connections = degree_dict[persona]
    # Scale size based on connection count
    scaled_size = scale_size(
        connections,
        min_persona_degree,
        max_persona_degree,
        PERSONA_MIN_SIZE,
        PERSONA_MAX_SIZE
    )
    persona_size.append(scaled_size)
    
    # Hover text
    companies = gr_net_f.filter(pl.col('persona_id') == persona)['company_id'].to_list()
    persona_name = df_all_personas.filter(pl.col('persona_id')==persona)['full_name'][0].title()
    hover_text = f"<b>Persona:</b> {persona_name}<br>"
    hover_text += f"<b>Companies worked at:</b> {connections}<br>"
    if connections > 0:
        # Check if worked at highlighted company
        if highlighted_company:
            worked_at_highlighted = highlighted_company in companies
            if worked_at_highlighted:
                hover_text += f"<b>Worked at {highlighted_company}:</b> ✓<br>"
        
        hover_text += "<br>".join([f"  • {comp}" for comp in companies[:5]])
        if connections > 5:
            hover_text += f"<br>  • ... and {connections - 5} more"
    persona_hover.append(hover_text)

# Create company node trace
company_trace = go.Scatter(
    x=company_x, y=company_y,
    mode='markers+text',
    hoverinfo='text',
    hovertext=company_hover,
    text=company_text,
    textposition="top center",
    textfont=dict(size=14, color='black'),
    marker=dict(
        color=company_color,
        size=company_size,
        line=dict(
            width=company_border_width,
            color=company_border_color
        ),
        opacity=0.9)
)

# Create persona node trace
persona_trace = go.Scatter(
    x=persona_x, y=persona_y,
    mode='markers',
    hoverinfo='text',
    hovertext=persona_hover,
    text=None,  # No text for personas
    marker=dict(
        color=persona_color,
        size=persona_size,
        line=dict(width=1, color='black'),
        opacity=0.7)
)

# Calculate axis ranges for 1:1 aspect ratio
all_positions = list(pos.values())
x_vals = [p[0] for p in all_positions]
y_vals = [p[1] for p in all_positions]

# Add padding
x_range = [min(x_vals) - 0.5, max(x_vals) + 0.5]
y_range = [min(y_vals) - 0.5, max(y_vals) + 0.5]

# Make axes have the same range for 1:1 aspect
max_range = max(x_range[1] - x_range[0], y_range[1] - y_range[0])
x_center = (x_range[0] + x_range[1]) / 2
y_center = (y_range[0] + y_range[1]) / 2

x_range = [x_center - max_range/2, x_center + max_range/2]
y_range = [y_center - max_range/2, y_center + max_range/2]

# Create figure with 1:1 aspect ratio
fig = go.Figure(data=[edge_trace, persona_trace, company_trace],
                layout=go.Layout(
                    title=f'Persona-Company Network (Companies Ordered by Size)<br><sup>Highlighted: {highlighted_company if highlighted_company else "None"}</sup>',
                    showlegend=False,
                    hovermode='closest',
                    margin=dict(b=20, l=20, r=20, t=100),
                    xaxis=dict(
                        showgrid=False, 
                        zeroline=False, 
                        showticklabels=False,
                        range=x_range,
                        scaleanchor="y",
                        scaleratio=1
                    ),
                    yaxis=dict(
                        showgrid=False, 
                        zeroline=False, 
                        showticklabels=False,
                        range=y_range
                    ),
                    plot_bgcolor='white',
                    paper_bgcolor='white',
                    width=900,
                    height=900
                ))

# Add legend with size examples and highlighting info
# legend_text = f"""
# <b>Node Size = Connection Count</b><br>
# <span style='color:#EF553B'>● Companies</span><br>
# <span style='color:#636efa'>● Personas</span> (hover for details)
# """

# fig.add_annotation(
#     x=0.98, y=0.98,
#     xref="paper", yref="paper",
#     text=legend_text,
#     showarrow=False,
#     font=dict(size=14),
#     align="left",
#     bgcolor="rgba(255, 255, 255, 0.95)",
    
# )

# Add top companies list
top_companies = company_ids_sorted[:10]  # Top 10 companies
top_companies_text = "<b>Top Companies by Connections:</b><br>"
for i, company in enumerate(top_companies, 1):
    connections = degree_dict[company]
    top_connections = degree_dict[top_companies[0]]
    connections_per = f" | {round(connections/top_connections*100)}%" if highlighted_company and company != highlighted_company else ""
    highlight_indicator = " " if highlighted_company and company == highlighted_company else ""
    top_companies_text += f"{dict_company_rev[company]}. {company}: {connections} {connections_per} {highlight_indicator}<br>"

fig.add_annotation(
    x=0.02, y=0.98,
    xref="paper", yref="paper",
    text=top_companies_text,
    showarrow=False,
    font=dict(size=14),
    align="left",
    bgcolor="rgba(255, 255, 255, 0.9)",
    # bordercolor="#666",
    # borderwidth=1
)

# Add probabiliy list

fig.add_annotation(
    x=0.98, y=0.98,
    xref="paper", yref="paper",
    text=annon_prob_text,
    showarrow=False,
    font=dict(
        family="'Courier New', monospace",  # Multiple fallbacks
        size=12,
        color="black"
    ),
    align="left",
    bgcolor="rgba(255, 255, 255, 0.95)",
    
)
fig.write_image((path_output_images/f'network_{current_company_id}.webp'))
fig.show()

amount = 5

tmp = df_full_personas_who_worked_in_company.sort(
    ["inferred_salary", "linkedin_connections", "inferred_years_experience"],
    descending=True,
)
tmp_gr = df_full_personas_experiences_plus.group_by('persona_id').agg(pl.len().alias('experience_count'))
tmp = df_full_personas_who_worked_in_company.join(tmp_gr, on='persona_id').sort('experience_count',descending=True)

tmp2 = pl.concat(
    [tmp.filter(pl.col('still_associated')==True, pl.col('higher_up')==True)[:amount*2],
     tmp.filter(pl.col('still_associated')==True, pl.col('higher_up')==False)[:amount*2],
     tmp.filter(pl.col('still_associated')==False, pl.col('higher_up')==True)[:amount*1],
     tmp.filter(pl.col('still_associated')==False, pl.col('higher_up')==False)[:amount*1],
    ]
).sort("full_name")

list_persona_for_plot = tmp2['persona_id'].to_list()

# Workforce data

def def_plotly_experience_range(current_persona_id):
    tmp_df = (df_full_personas_who_worked_in_company
              .filter(pl.col('persona_id')==current_persona_id)
              .with_columns(pl.col('end_year').fill_null(2021))['start_year','end_year'])
    
    fig_tmp = copy.deepcopy(fig_company_hiring_trend)
    fig_tmp.add_vrect(
        x0=tmp_df[0,'start_year'],
        x1=tmp_df[0,'end_year'],
        fillcolor="blue",
        opacity=0.1,
        line_width=0 
    )
    return fig_tmp

def def_plotly_experience_gantt(current_persona_id):
    px_data = (df_full_personas_experiences_plus
               .filter(pl.col('persona_id')==current_persona_id)
               .with_columns(
                   pl.col('end_date').fill_null(datetime.datetime(2020, 1, 1, 0,0)),
                   pl.col('company_name').str.to_uppercase(),
                   # pl.col('company_name').str.to_uppercase().str.replace_all('&', '-and-')
               )
               .sort('start_date'))
    
    y_order = px_data['company_name'].to_list()
    
    fig = px.timeline(px_data,x_start="start_date", x_end="end_date", y="company_name",
                      color='target',hover_data=["title_name"], height=140+30*len(px_data),
                      category_orders={"company_name": y_order},
                      color_discrete_map={True:'#EF553B',  False:'#636efa'},
                      labels={'target':'Target', 'start_date':'Recruited', 'end_date':'If-Resigned', 
                             'company_name':'Company', 'title_name':'Job role'}
                     # title=f"Experience of {current_persona_name}.",
                     )
    fig.update_yaxes(
        # autorange="reversed",
                              showgrid=True,
                              gridcolor='lightgray',
                              gridwidth=1,
                              griddash='dot'
    )
    fig.update_layout(showlegend=False, xaxis_title=None, yaxis_title=None)
    return fig

4 Workforce sample

4.1 Abhishek Madhavan

Job title: Lead product marketing manager
Socials: https://angel.co/abhishek-madhavan | https://facebook.com/abhishek.madhavan.96 | https://linkedin.com/in/abhishekmadhavan | https://twitter.com/abhishekmadhavn

4.1.1 Abhishek Madhavan’s working period at freshworks inc. (formerly freshdesk)

4.1.2 Gantt plot of Abhishek Madhavan’s experience

---

4.2 Adit Parekh

Job title: Senior manager corporate development
Socials: https://foursquare.com/user/8652453 | https://quora.com/adit-parekh | https://angel.co/adit-parekh | https://linkedin.com/in/adit-parekh-36940014 | https://flickr.com/people/aditparekh | https://facebook.com/aditparekh | https://klout.com/aditparekh | https://gravatar.com/aditparekh | https://github.com/aditparekh | https://linkedin.com/in/aditparekh | https://twitter.com/goodeartht

4.2.1 Adit Parekh’s working period at freshworks inc. (formerly freshdesk)

4.2.2 Gantt plot of Adit Parekh’s experience

---

4.3 Ali Murphy

Job title: Customer service and sales representative
Socials: https://facebook.com/ali.murphy.184 | https://twitter.com/ali_murphy27 | https://linkedin.com/in/alimariemurphy

4.3.1 Ali Murphy’s working period at freshworks inc. (formerly freshdesk)

4.3.2 Gantt plot of Ali Murphy’s experience

---

4.4 Amiya Adwitiya

Job title: Bizops and ceos office
Socials: https://quora.com/amiya-adwitiya | https://linkedin.com/in/amiya-adwitiya-19690714 | https://facebook.com/amiya.adwitiya

4.4.1 Amiya Adwitiya’s working period at freshworks inc. (formerly freshdesk)

4.4.2 Gantt plot of Amiya Adwitiya’s experience

---

4.5 Andrews Moses

Job title: Senior consultant and operations canada
Socials: https://linkedin.com/in/canadacloudconsultant | https://linkedin.com/in/freshdeskcanada | https://linkedin.com/in/freshdeskconsultant

4.5.1 Andrews Moses’s working period at freshworks inc. (formerly freshdesk)

4.5.2 Gantt plot of Andrews Moses’s experience

---

4.6 Anna Hantelmann

Job title: Freelance consultant
Socials: https://linkedin.com/in/anna-luisa-hantelmann

4.6.1 Anna Hantelmann’s working period at freshworks inc. (formerly freshdesk)

4.6.2 Gantt plot of Anna Hantelmann’s experience

---

4.7 Arjun Sukumaran

Job title: Content writer
Socials: https://youtube.com/user/alpha1156 | https://linkedin.com/in/arjun-sukumaran | https://quora.com/arjun-sukumaran | https://linkedin.com/in/arjun-sukumaran-8828344a | https://facebook.com/arjun.sukumaran | https://twitter.com/arjunsukumaran

4.7.1 Arjun Sukumaran’s working period at freshworks inc. (formerly freshdesk)

4.7.2 Gantt plot of Arjun Sukumaran’s experience

---

4.8 Ashwin Ramesh

Job title: Lead frontend engineer
Socials: https://plus.google.com/+ashwinramesh | https://foursquare.com/user/3368107 | https://vimeo.com/user/9212235 | https://facebook.com/ash.mirage | https://angel.co/ashwin-ramesh | https://linkedin.com/in/ashwin-ramesh-2b256919 | https://twitter.com/ashwinatnyc | https://klout.com/ashwinatnyc | https://about.me/ashwinramesh | https://youtube.com/user/ashwinramesh88 | https://github.com/mirage88 | https://gravatar.com/mirage88 | https://linkedin.com/in/rameshashwin | https://stackoverflow.com/users/1037952

4.8.1 Ashwin Ramesh’s working period at freshworks inc. (formerly freshdesk)

4.8.2 Gantt plot of Ashwin Ramesh’s experience

---

4.9 Christopher Wiltberger

Job title: General manager - southern europe
Socials: https://linkedin.com/in/chris-wiltberger | https://linkedin.com/in/chris-wiltberger-9063a | https://linkedin.com/in/christopher-wiltberger-9063a

4.9.1 Christopher Wiltberger’s working period at freshworks inc. (formerly freshdesk)

4.9.2 Gantt plot of Christopher Wiltberger’s experience

---

4.10 Daniel Nath

Job title: Project intern
Socials: https://linkedin.com/in/daniel-nath-a6319bb8 | https://facebook.com/daniel.nath.9 | https://linkedin.com/in/danielnath | https://linkedin.com/in/danielnath17

4.10.1 Daniel Nath’s working period at freshworks inc. (formerly freshdesk)

4.10.2 Gantt plot of Daniel Nath’s experience

---

4.11 Danielle Murphy

Job title: Executive assistant
Socials: https://linkedin.com/in/danielle-murphy-a512866 | https://facebook.com/danielle.a.murphy.1 | https://twitter.com/danielle94070 | https://linkedin.com/in/daniellemurphy

4.11.1 Danielle Murphy’s working period at freshworks inc. (formerly freshdesk)

4.11.2 Gantt plot of Danielle Murphy’s experience

---

4.12 Dhawal Kumar

Job title: Growth and marketing
Socials: https://linkedin.com/in/dhawalkumar95 | https://linkedin.com/in/dhwlkmr | https://facebook.com/dhwlkmr

4.12.1 Dhawal Kumar’s working period at freshworks inc. (formerly freshdesk)

4.12.2 Gantt plot of Dhawal Kumar’s experience

---

4.13 Duncan Travis

Job title: Engagement manager
Socials: https://linkedin.com/in/duncantravis

4.13.1 Duncan Travis’s working period at freshworks inc. (formerly freshdesk)

4.13.2 Gantt plot of Duncan Travis’s experience

---

4.14 Eli Thomas

Job title: Account executive
Socials: https://linkedin.com/in/eli-thomas-7769a838 | https://facebook.com/eli.thomas.3367 | https://linkedin.com/in/elithomas

4.14.1 Eli Thomas’s working period at freshworks inc. (formerly freshdesk)

4.14.2 Gantt plot of Eli Thomas’s experience

---

4.15 Erik Bromley

Job title: Commercial account executive
Socials: https://linkedin.com/in/erik-bromley-40086339 | https://facebook.com/erik.bromley

4.15.1 Erik Bromley’s working period at freshworks inc. (formerly freshdesk)

4.15.2 Gantt plot of Erik Bromley’s experience

---

4.16 Indeevar Krishna

Job title: Vice president
Socials: https://linkedin.com/in/indeevar-krishna-507a7b4

4.16.1 Indeevar Krishna’s working period at freshworks inc. (formerly freshdesk)

4.16.2 Gantt plot of Indeevar Krishna’s experience

---

4.17 Joe Massian

Job title: Enterprise sales
Socials: https://facebook.com/jmassian | https://linkedin.com/in/jmassian | https://linkedin.com/in/joe-massian-b104833

4.17.1 Joe Massian’s working period at freshworks inc. (formerly freshdesk)

4.17.2 Gantt plot of Joe Massian’s experience

---

4.18 Karthick Sundaram

Job title: Director of engineering
Socials: https://linkedin.com/in/karthick-sundaram-5263616 | https://linkedin.com/in/karthicks13 | https://linkedin.com/in/karthicksundaram | https://github.com/karthicksundaram

4.18.1 Karthick Sundaram’s working period at freshworks inc. (formerly freshdesk)

4.18.2 Gantt plot of Karthick Sundaram’s experience

---

4.19 Lauren Miller

Job title: Recruiter
Socials: https://linkedin.com/in/lauren-miller-88778925 | https://facebook.com/lauren.miller.92754

4.19.1 Lauren Miller’s working period at freshworks inc. (formerly freshdesk)

4.19.2 Gantt plot of Lauren Miller’s experience

---

4.20 Marina Sweeney

Job title: Corporate recruiter
Socials: https://linkedin.com/in/marina-sweeney-722a3 | https://linkedin.com/in/marinasw | https://facebook.com/marinasw

4.20.1 Marina Sweeney’s working period at freshworks inc. (formerly freshdesk)

4.20.2 Gantt plot of Marina Sweeney’s experience

---

4.21 Miguel Adao

Job title: Head of marketing, north america
Socials: https://facebook.com/miguel.adao | https://linkedin.com/in/migueladao

4.21.1 Miguel Adao’s working period at freshworks inc. (formerly freshdesk)

4.21.2 Gantt plot of Miguel Adao’s experience

---

4.22 Nirmaan Agrawal

Job title: Pre-sales consultant
Socials: https://linkedin.com/in/nirmaan-agrawal-4bb1943 | https://quora.com/nirman-agrawal | https://linkedin.com/in/nirman-agrawal-4bb1943 | https://youtube.com/user/nirmanagrawal | https://facebook.com/nirmanagrawal

4.22.1 Nirmaan Agrawal’s working period at freshworks inc. (formerly freshdesk)

4.22.2 Gantt plot of Nirmaan Agrawal’s experience

---

4.23 Nivas Ravichandran

Job title: Startup partnerships and growth lead
Socials: https://quora.com/nivas-ravichandran | https://angel.co/nivas-ravichandran | https://twitter.com/nivasravi | https://youtube.com/user/nivasravichandran | https://linkedin.com/in/nivasravichandran | https://facebook.com/nivasravichandran

4.23.1 Nivas Ravichandran’s working period at freshworks inc. (formerly freshdesk)

4.23.2 Gantt plot of Nivas Ravichandran’s experience

---

4.24 Pranav Mehra

Job title: Associate general counsel
Socials: https://twitter.com/indianantitrust | https://linkedin.com/in/pranavmehra

4.24.1 Pranav Mehra’s working period at freshworks inc. (formerly freshdesk)

4.24.2 Gantt plot of Pranav Mehra’s experience

---

4.25 Saurav Chakravorty

Job title: Director - data science
Socials: https://youtube.com/user/captainofsoul | https://gravatar.com/csaurav | https://angel.co/saurav-chakravorty | https://linkedin.com/in/saurav-chakravorty-3266a86 | https://github.com/saurav2608 | https://twitter.com/sauravchakr | https://linkedin.com/in/sauravchakravorty

4.25.1 Saurav Chakravorty’s working period at freshworks inc. (formerly freshdesk)

4.25.2 Gantt plot of Saurav Chakravorty’s experience

---

4.26 Shekhar Kirani

Job title: Board member
Socials: https://linkedin.com/in/kirani | https://crunchbase.com/person/shekhar-kirani | https://linkedin.com/in/shekhar-kirani-2b2a473 | https://facebook.com/shekharkirani | https://twitter.com/skirani

4.26.1 Shekhar Kirani’s working period at freshworks inc. (formerly freshdesk)

4.26.2 Gantt plot of Shekhar Kirani’s experience

---

4.27 Srividya Sriram

Job title: Design manager
Socials: https://facebook.com/srisriram | https://crunchbase.com/person/srividya-sriram | https://dribbble.com/srividyasriram | https://twitter.com/srividyasriram | https://about.me/srividyasriram | https://linkedin.com/in/srividyasriram | https://angel.co/srividyasriram

4.27.1 Srividya Sriram’s working period at freshworks inc. (formerly freshdesk)

4.27.2 Gantt plot of Srividya Sriram’s experience

---

4.28 Subhash Chandran

Job title: Technical architect
Socials: https://vimeo.com/user/450625 | https://foursquare.com/user/964397 | https://linkedin.com/in/subhash-chandran-5971582 | https://facebook.com/subwiz | https://github.com/subwiz | https://reddit.com/user/subwiz | https://flickr.com/people/subwiz | https://klout.com/subwiz | https://about.me/subwiz | https://linkedin.com/in/subwiz | https://twitter.com/subwiz | https://stackoverflow.com/users/1289628

4.28.1 Subhash Chandran’s working period at freshworks inc. (formerly freshdesk)

4.28.2 Gantt plot of Subhash Chandran’s experience

---

4.29 Susan Callaghan

Job title: Senior recruiter
Socials: https://linkedin.com/in/susan-callaghan-84a301 | https://facebook.com/susan.callaghan.961

4.29.1 Susan Callaghan’s working period at freshworks inc. (formerly freshdesk)

4.29.2 Gantt plot of Susan Callaghan’s experience

---

4.30 Vikram Naval

Job title: Enterprise solution engineer
Socials: https://quora.com/vikram-naval | https://angel.co/vikram-naval | https://facebook.com/vikramnaval | https://linkedin.com/in/vikramnaval | https://twitter.com/vikramnaval

4.30.1 Vikram Naval’s working period at freshworks inc. (formerly freshdesk)

4.30.2 Gantt plot of Vikram Naval’s experience

---

df_full_personas_who_worked_in_company.write_parquet(current_company_parquet)